The twentieth century, particularly, saw the exponential growth of urban regions throughout the world, and in its latter half, the quantum development of suburban districts around the peripheries of cities, fuelled by expressways and the dominance of the automobile-based society. This condition, in which the majority of North Americans, for example, now live in suburbs with low rates of built density and human activity, is generally unable economically to sustain mass transportation. Residence, work, shopping and leisure are not only low in density, and highly land consuming, but activities are generally segregated and separate. In consequence, there is now widespread concern for the effects of such dependence on the automobile—in air pollution, greatly increasing delays, in the increasing aggregate traveling that decreases the quality of peoples lives in costs, time and difficulties in getting to jobs, and in many other respects.
A range of policies and practices have been promoted to deal with this situation, developing forms of land use and transportation in combination, so as to conserve energy, minimize emissions of pollutants, encourage accessibility while minimizing mobility—for example, by developing intensive activity centres around public transport nodes. Regions around the globe are involved in efforts to translate these ambitions into regional strategic development frameworks.
In general, as the world population becomes more concentrated in urban regions, the quality of life in any given urban region is greatly affected by how well the urban region is equipped with infrastructure to support the needs of the local population. Urban planning is a well-known discipline that is used to plan how such infrastructure is added, replaced and maintained. Urban planning also encompasses a number of other issues as will occur to those of skill in the art.
At least in developed countries, most urban regions implement varying degrees of urban planning. The process is often heavily influenced by political factors, as issues around taxation and property rights are necessarily intertwined with the urban planning process. Recently in North America, there has been a trend towards “lean government” policies, wherein government-based centralized urban planning is largely abandoned in favour of allowing the urban region to grow in a laissez-faire manner, on the belief that the free market is the best determiner as to how the area should grow. Still other administrations may implement a more activist policies, involving a great deal of centralized planning, with the view that government controlled central planning is the most efficient way to serve the needs of the local population. Of course, the approach for any given region usually lies between these extremes. Regardless of the chosen approach, one problem with prior art urban mapping and data collection techniques is that there is little in the way of hard-data that can be analyzed to provide an objective view as to how urban planning can be implemented most effectively.
The hard-data that exists today, which has been collected inconsistently across a region, suggests that more data, and the right kind of data, could be extremely effective in urban planning. For example, as of 2003, it is known that the city of Toronto has a subway system that supports itself largely out of the fare-box, with little reliance on government subsidies. It is hypothesized that a major factor contributing to this phenomenon is that there is a large population density that lives (“residential district”) adjacent to subway stations, and there is at least one concentrated area in the downtown core where that population works (“employment district”) that is also adjacent to subway stations. A similar phenomenon can be observed in New York. The effort required to generate a report to support this hypothesis, however, is enormous, complex, time-consuming and costly. As one approach, the effort could involve collecting street maps and subway maps of Toronto, and then conducting door-to-door surveys in both the residential and employment districts to verify that people are actually using the subways to commute to work. Finally, the data collected from the door-to-door surveys may then be correlated with the maps to ultimately arrive at a report with a conclusion that supports the hypothesis. However, it can be noted that the report includes only a few sets of data points, and does not include other data that may influence whether or not simple densities of residential districts and employment districts is sufficient to support subway lines. Such a report also does not describe the structure of the built environment which dictates the densities. Further, such a report is not readily comparable with how other Urban regions handle transport from residential districts to employment districts, to provide an objective assessment as to which urban region is best handling its transportation needs. More complex questions as to how a particular urban region functions in relation to another will occur to those of skill in the art, and the generation of reports to answer such questions will face similar hurdles and complexities.
As previously mentioned, prior art urban maps are a very useful element in the generation of the above-described type of report for urban planning exercises. Prior art urban maps principally identify physical characteristics of transportation routes, and include identifiers like street names and station names on those maps. The maps may include indications as to whether a particular area is more dominated by residential, commercial or industrial activity, but little more. In general, such maps are very useful for navigating the urban region, but provide limited information when attempting to generate complex reports for urban planning.
More recent urban maps of the prior art offer information that can be used for more than simply navigating the urban region. These maps are generated at least in part, using remotely sensed data obtained from satellites, air-planes and the like. Baltsavias, Emmanuel P. and A. Gruen. “Resolution Convergence: A comparison of aerial photos, LIDAR and IKONOS for monitoring cities” in Remotely Sensed Cities, edited by Victor Mesev, Taylor & Francis, London, 2003 (“Baltsavias”) is one prior art reference that discloses an example of such an urban map. Baltsavias includes a review and evaluation of the use of current high-resolution remote sensing technologies including aerial/digital orthoimagery, Laser-Induced Detection and Ranging (“LIDAR”), IKONOS (4-meters per pixel colour and 1 meter per pixel black-and-white optical satellite imagery) to extract geo-spatial information such as:                1) digital terrain models (“DTM”, an elevation model that is a representation of the bare surface of the earth with natural and manmade features removed.);        2) digital surface models (“DSM”, also referred to as a “first surface” model in which man-made and natural features are captured in the elevation model.); and,        3) an identification of urban objects such as buildings, roads, vegetation, etc, and reconstruction of three-dimensional urban objects such as buildings.Baltsavias describes requirements for developing three-dimensional city models and briefly describes two commercial applications that have been developed, InJECT, a product of INPHO GmbH, Stuggart, Germany and CyberCity Modeler (CC-Modeler) marketed by CyberCity AG, Bellikon, Switzerland. Baltsavias describes a prototype system, CyberCity Spatial Information System (“CC-SIS”) which is an attempt to integrate three dimensional city models with a relational database that can be potentially linked to external Geographic Information Systems (“GIS”) data. In order to identify objects, the user manually identifies points onscreen, and only then will the application automatically build topology that includes the geometry needed to relate those points and identify an object. The application requires the use of digital orthophotos which are costly to acquire at the resolution that is necessary to build the city model. Further, Baltsavias does not explain how to derive building use or type and its relation to other buildings in its immediate proximity or at the city-wide scale. The application does not allow a user to assess how a region functions or compares to other urban regions. In general, Baltsavias is limited in how it offers to describe and visualize an urban region's composition and functions.        
Another example of increased urban map sophistication is found in Barnsley, Michael J., A. M. Steel, and S. Barr. “Determining urban land use through an analysis of the spatial composition of buildings identified in LIDAR and multispectral image data,” in Remotely Sensed Cities, edited by Victor Mesev. Taylor & Francis, London, 2003. (“Barnsley”). Barnsley uses a combination of IKONOS at 4 meters per pixel colour satellite imagery and LIDAR (2 m) image data at 0.4 point sampling density per square-meter, to extract the existence of building objects from other surrounding objects, such as trees or paved roads. The results of the extraction were compared to base data to gage accuracy of results. Four test areas are used where the predominant land use is either residential or industrial. Given the limitations of the data sets several thresholds were applied to the data to improve the results. Barnsley develops a graph-based pattern recognition system to infer land use by height and structural configuration. The technology and techniques used in Barnsley to extract building objects semi-automatically and to identify differences in morphological properties of buildings and the structural composition of built form patterns were successful in differentiating general land use types, (e.g. residential versus industrial), but there were problems in identifying and characterizing unique patterns within these general land use types, different residential and industrial patterns were not able to be characterized given the measurement techniques used. In general, Barnsley does not teach how to classify and describe the unique built form for different residential and industrial uses.
An example of an as-yet unfulfilled attempt to provide a more sophisticated urban map is found in Eguchi, Ronald, C. Huyck, B. Houshmand, D. Tralli, and M. Shinozuka. “A New Application of Building Inventories using Synthetic Aperture Radar Technology.”, presented at the 2nd Multi-Lateral Workshop on Development of Earthquake and Tsunami Disasters Mitigation Technologies and their Integration for the Asia-Pacific region. Mar. 1-2, 2000. Kobe, Japan. (“Eguchi”). Using Interferometric Synthetic Aperture Radar (IFSAR) airborne technology, aerial photography and county tax assessment data, Eguchi attempts to identify building types based on building footprint and height which they extract from the remotely sensed data and validate results using county tax assessment data. The preliminary results of the techniques used and future research plans are presented in Eguchi, laying the groundwork to work towards a building inventory at a city-wide scale from which they can measure building density and development. Despite the groundwork that has been laid, there is no indication of success or how such success will be achieved.
Another example is Mesev, Victor. “Urban Land Use Reconstruction: Image Pattern Recognition from Address Point Information.”, presented at the International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences Conference, Regensburg, Germany, 27-29 Jun., 2003. (“Mesev”). Mesev examines the use of address point data collected by the Ordnance Survey in the UK to examine spatial patterns of development in Bristol UK. The address point data contains information on general land use types, residential versus commercial, and Mesev attempts to identify differences between different areas of the same land use type, e.g. residential #1 and residential #2, based on a various spatial indices/techniques, i.e. density of points and nearest neighborhood analysis. This information from this spatial recognition system is used to inform multispectral image classifications of urban regions. Mesev introduces some preliminary results used on fine resolution aerial photography provided by a company called Cities Revealed (The GeoInformation Group, Telford House, Fulbourn, Cambridge, CB1 5HB, United Kingdom—http://www.crworld.co.uk). The remote sensed imagery for Cities Revealed is quite costly to acquire for a large urban region. The data used for the pattern recognition is unique to the UK but not available for all regions, since the UK can rely so heavily on the UK Ordnance Survey. Likewise the spatial indices are not fully successful on other urban land use classes such as commercial and industrial where information on building characteristics would be more useful than just the arrangement of buildings.